Too Much Or Too Little Physical Activity Accelerates Brain Aging

Researchers have discovered that the amount of physical activity we do can influence brain aging. Using imaging tests and devices that accurately measure body movements, they found that both too little and too much physical activity can accelerate brain aging. The key is balance: doing a moderate amount of exercise, at any intensity, helps keep the brain younger and healthier throughout life.

Aging is a natural process that affects each person differently, and this is due to a combination of genetic factors (i.e., what we inherit from our parents) and environmental factors (such as lifestyle, diet, stress, and exposure to pollutants, for example). Of all organs, the brain is one of the most sensitive to the effects of aging.

This is because its structure changes throughout life; it shrinks in certain areas, loses connections between neurons, and may even function more slowly. These changes do not occur in the same way in everyone. Some people maintain a healthier brain for longer, while others show signs of brain aging earlier than expected.

Based on these differences, scientists have begun using MRI scans to study these structural changes and attempt to predict a person's "brain age." This is done with the help of machine learning algorithms, a type of artificial intelligence trained to recognize patterns, that analyze data from thousands of people of different ages.

When the algorithm can estimate a person's age simply by looking at their brain, we can compare this "brain age" with their actual (chronological) age. If the brain appears older than its actual age, we say that accelerated brain aging has occurred. The difference between these two ages is called the Brain Age Gap (BAG).

This brain age prediction tool has become very useful for studying diseases such as dementia, depression, schizophrenia, and even the risk of premature death. However, despite these advances, we still know little about how everyday factors, such as diet, sleep quality, education, smoking, or physical activity, influence brain age.

Discovering which of these factors can be modified is essential for developing ways to prevent or delay brain aging and maintain mental health for longer.

One of these modifiable factors is physical activity. Several studies have shown that more active people tend to have healthier brains, with preserved structures, such as a larger volume of the hippocampus (a region linked to memory) and gray matter (where neurons are located).

However, most of these studies used questionnaires to measure how much people exercised, which may not be as reliable. After all, people don't always accurately remember how much they exercised, or they tend to overestimate their activity. Furthermore, little is known about how different intensities of activity (light, moderate, or intense) objectively affect brain aging.

To address this problem, scientists have begun using accelerometers, which are small devices, like wristwatches, that accurately record body movement over several days.

This allows them to know exactly how much time a person spent moving, how intensely, and at what times of day. In the study described here, researchers used data from the UK Biobank, the world's largest database of brain scans and accelerometer-based physical activity records.

This study analyzed data from nearly 17,000 people with MRI brain scans and accelerometer-based physical activity records over seven days. To estimate each participant's brain age, the scientists used an advanced artificial intelligence method called Light Gradient-Boosting Machine (LightGBM), which is highly effective at handling large volumes of data and making accurate predictions.

They used more than 1,400 brain characteristics (such as the size of specific regions and their density) to train the model. Then, they calculated the difference between the estimated brain age and the actual age (the BAG).

Additionally, the researchers divided participants' physical activity into four categories: light physical activity (such as walking slowly), moderate activity (such as walking at a faster pace), vigorous activity (such as running or intense exercise), and a combined moderate-to-vigorous category. They then analyzed whether there was a relationship between the amount of time spent at each of these intensities and brain age.

The results were very interesting. The artificial intelligence model performed excellently in predicting brain age (with a very high correlation between predicted and actual age). Most important, however, was the pattern found in the relationship between physical activity and brain age: a U-shaped curve.

This means that both a lack of physical activity and excess physical activity (at any intensity) were associated with accelerated brain aging. In other words, too little exercise is bad, but too much can also be harmful. The ideal amount appears to be a moderate level, which promotes brain health and reduces the risk of premature aging.

Furthermore, the researchers observed that brain age was related to cognitive performance (such as memory and attention) and other mental health issues. This suggests that maintaining a balanced physical activity can not only preserve brain structure but also its functions, helping to prevent neurological and psychiatric diseases.

Finally, this study reinforces the importance of objective devices, such as accelerometers, to accurately measure physical activity. This is even more relevant considering the growing popularity of smartwatches and wristbands, which are now widely available.

These devices can be used to monitor and adjust lifestyle habits in a personalized way, promoting healthier brain aging and helping to prevent mental and cognitive disorders as we age.

READ MORE:

Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age

Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang, and Chenjie Xu

Health Data Science. 2 May 2025, Vol 5 Article ID: 0257

DOI: 10.34133/hds.0257

Abstract: 

Background: A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. This study aimed to investigate the associations between accelerometer-measured PA and brain age. Methods: A total of 16,972 eligible participants with both valid T1-weighted neuroimaging and accelerometer data from the UK Biobank was included. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM). Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders. Results: The brain age estimated by LightGBM achieved an appreciable performance (r = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (r = 0.90, MAE = 3.03). We found that LPA (F = 2.47, P = 0.04), MPA (F = 6.49, P < 1 × 10−300), VPA (F = 4.92, P = 2.58 × 10−5), and MVPA (F = 6.45, P < 1 × 10−300) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Furthermore, mediation analysis suggested that BAG partially mediated the associations between PA and cognitive functions as well as brain-related disorders. Conclusions: Our study revealed a U-shaped association between accelerometer-measured PA and BAG, highlighting that advanced brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.